Okay, I've searched for Lead Data Engineer roles in Jersey City, NJ, requiring 10+ years of experience, expertise in Snowflake and AWS Glue, and full-time job mode. I have found 47 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Lead Data Engineer role is fundamentally about owning and delivering complex, end-to-end data engineering projects that drive reliable, scalable, and high-performing data ecosystems within a hybrid, enterprise-scale environment. The candidate is expected to bring deep hands-on expertise in Snowflake, SQL, Python, Spark, and AWS ecosystem tools such as Glue and EMR, which are critical for managing large-scale, cloud-based data lakes and warehouses. Beyond just technical proficiency, the role demands mastery over data ingestion, transformation (ETL), security (RBAC, row/column-level protections), and real-time data streaming concepts like CDC, underscoring the need to maintain data integrity and performance at scale. The emphasis on query optimization, performance tuning, and debugging signals challenges in optimizing massive datasets and ensuring operational stability under heavy loads. Leadership is central here—not just managing a team of 8-10 engineers but serving as a pivotal liaison to stakeholders and cross-functional partners, ensuring collaboration drives project clarity, change management, and knowledge transfer. Agile methodology experience implies a dynamic, iterative delivery cycle, requiring flexibility and clear communication. Success in this role means not only delivering robust data pipelines but anticipating and resolving data quality issues, orchestrating seamless deployments, and fostering a sustainable technology and team culture that scales business intelligence capabilities.

Company Analysis:

EXL operates as a strategic partner focused on leveraging data and analytics to drive practical business transformation rather than simply implementing off-the-shelf solutions. This positions the company as a collaborative, customer-centric, and insight-driven organization thriving in digital evolution and AI-enablement. The culture likely values adaptability, deep listening, and bespoke problem-solving over rigid process adherence, fostering an environment that prizes innovation balanced with operational pragmatism. For a Lead Data Engineer, this means that technical excellence must be coupled with strong interpersonal skills to align complex data solutions with unique client needs and legacy systems. Externally, EXL’s market position as a transformation and analytics specialist gives the role a strategic influence on client outcomes, requiring the engineer to consider not just technical delivery but also business impact. The role likely features high visibility within delivery teams and clients’ business leaders, blending technical leadership with consultative collaboration. Moreover, since EXL prioritizes efficient, scalable, and sustainable models, this role is critical in ensuring data architectures not only perform but evolve alongside shifting business strategies and technological advances.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you led a data engineering project that faced significant technical challenges. How did you approach problem-solving and motivate your team through it?
• Behavioral: Describe an experience where you had to collaborate with multiple stakeholders with differing priorities. How did you manage expectations and ensure project success?
• Behavioral: Share a specific example of how you handled a critical data quality issue in a production environment. What steps did you take to diagnose and fix the problem?
• Behavioral: Explain how you have supported knowledge transfer and training within your teams. Why do you think this is important in a leadership role?
• Technical: How do you approach query optimization in Snowflake to improve the performance of large-scale data processing workloads?
• Technical: Walk me through your design and implementation strategy for building a secure, scalable ETL pipeline using AWS Glue and Apache Spark.
• Technical: What methods and tools do you use to implement Change Data Capture (CDC) in data pipelines, and how do you ensure data consistency?
• Technical: Explain how you would architect role-based access control (RBAC) and row/column-level security in a data platform to meet enterprise compliance needs.
• Situational: Imagine you discover a major data processing bottleneck affecting production reports. How would you identify the root cause and communicate your remediation plan to stakeholders?
• Situational: Suppose your team is divided on the choice between implementing batch versus incremental data processing for a new pipeline. How would you facilitate consensus and make a decision?
• Situational: You are asked to onboard a legacy system’s data into Snowflake, but documentation is sparse and the data quality is uncertain. How would you proceed to ensure timely and reliable integration?
• Company Fit: What interests you most about working at EXL, particularly given our emphasis on tailored, outcome-driven data solutions?
• Company Fit: How do you see your leadership style aligning with EXL’s collaborative and adaptive culture?
• Company Fit: EXL emphasizes sustainable, scalable digital transformation rather than quick fixes. How have you contributed to long-term data architecture sustainability in past roles?
• Questions for Interviewer: Can you describe how the data engineering team collaborates with business stakeholders and analytics teams at EXL?
• Questions for Interviewer: What are the immediate data challenges EXL is facing that this Lead Data Engineer would be expected to tackle?
• Questions for Interviewer: How does EXL support ongoing professional development and skill growth for its technical leaders?